Neural Control Barrier Functions for Safe Navigation

Understand

Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial.

• This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments.
• A novel methodology to jointly learn CBFs and corresponding safe controllers, in simulation, inspired by the State Dependent Riccati Equation (SDRE) is proposed.
• The CBF is used to obtain admissible commands from any nominal, possibly unsafe controller.